IchinoFS.SDA: Ichino's feature selection method for symbolic data

In symbolicDA: Analysis of Symbolic Data

Ichino's feature selection method for symbolic data

Description

Ichino's method for identifiyng non-noisy variables in symbolic data set

Usage

IchinoFS.SDA(table.Symbolic)

Arguments

table.Symbolic

symbolic data table

Details

See file ../doc/IchinoFSSDA_details.pdf for further details

Value

plot	plot of the gradient illustrating combinations of variables, in which the axis of ordinates (Y) represents the maximum number of mutual neighbor pairs and the axis of the abscissae (X) corresponds to the number of features (m)
combination	the best combination of variables, i.e. the combination most differentiating the set of objects
maximum results	step-by-step combinations of variables up to m variables
calculation results	..............

Author(s)

Andrzej Dudek [e-mail], Justyna Wilk [e-mail] Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland http://keii.ue.wroc.pl/symbolicDA/

References

Ichino, M. (1994), Feature selection for symbolic data classification, In: E. Diday, Y. Lechevallier, P.B. Schader, B. Burtschy (Eds.), New Approaches in Classification and data analysis, Springer-Verlag, pp. 423-429.

Bock, H.H., Diday, E. (eds.) (2000), Analysis of symbolic data. Explanatory methods for extracting statistical information from complex data, Springer-Verlag, Berlin.

Diday, E., Noirhomme-Fraiture, M. (eds.) (2008), Symbolic Data Analysis with SODAS Software, John Wiley & Sons, Chichester.

See Also

HINoV.SDA; HINoV.Symbolic in clusterSim library

Examples

# LONG RUNNING - UNCOMMENT TO RUN
#data("cars",package="symbolicDA")
#sdt<-cars
#ichino<-IchinoFS.SDA(sdt) 
#print(ichino) 

symbolicDA documentation built on Feb. 16, 2023, 6 p.m.